About: Greedy Algorithm for a Training Set Reduction in the Kernel Methods

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	We propose a technique for a training set approximation and its usage in kernel methods. The approach aims to represent data in a low dimensional space with possibly minimal representation error which is similar to the Principal Component Analysis (PCA). In contrast to the PCA, the basis vectors of the low dimensional space used for data representation are properly selected vectors from the training set and not as their linear combinations. The basis vectors can be selected by a simple algorithm which has low computational requirements and allows on-line processing of huge data sets. The proposed method was used to approximate training sets of the Support Vector Machines and Kernel Fisher Linear Discriminant which are known method for learning classifiers. The experiments show that the proposed approximation can significantly reduce the complexity of the found classifiers (the number of the support vectors) while retaining their accuracy. We propose a technique for a training set approximation and its usage in kernel methods. The approach aims to represent data in a low dimensional space with possibly minimal representation error which is similar to the Principal Component Analysis (PCA). In contrast to the PCA, the basis vectors of the low dimensional space used for data representation are properly selected vectors from the training set and not as their linear combinations. The basis vectors can be selected by a simple algorithm which has low computational requirements and allows on-line processing of huge data sets. The proposed method was used to approximate training sets of the Support Vector Machines and Kernel Fisher Linear Discriminant which are known method for learning classifiers. The experiments show that the proposed approximation can significantly reduce the complexity of the found classifiers (the number of the support vectors) while retaining their accuracy. (en)
Title	Greedy Algorithm for a Training Set Reduction in the Kernel Methods Greedy Algorithm for a Training Set Reduction in the Kernel Methods (en)
skos:prefLabel	Greedy Algorithm for a Training Set Reduction in the Kernel Methods Greedy Algorithm for a Training Set Reduction in the Kernel Methods (en)
skos:notation	RIV/68407700:21230/03:03091283!RIV/2004/GA0/212304/N
http://linked.open.../vavai/riv/strany	426 ; 433
http://linked.open...avai/riv/aktivita	P Z
http://linked.open...avai/riv/aktivity	P(GA102/03/0440), Z(MSM 212300013)
http://linked.open...vai/riv/dodaniDat	2004
http://linked.open...aciTvurceVysledku	Hlaváč, Václav Franc, Vojtěch
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	608513
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/03:03091283
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	PCA;kernel methods;training set reduction (en)
http://linked.open.../riv/klicoveSlovo	PCA kernel methods training set reduction
http://linked.open...ontrolniKodProRIV	[CDB179C69E7E]
http://linked.open...v/mistoKonaniAkce	Groningen
http://linked.open...i/riv/mistoVydani	Berlin
http://linked.open...i/riv/nazevZdroje	CAIP 2003: Computer Analysis of Images and Patterns
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Recognizing human activities for automated video surveillance
http://linked.open...UplatneniVysledku	2003
http://linked.open...iv/tvurceVysledku	Hlaváč, Václav Franc, Vojtěch
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2003-08-25 (xsd:date)
http://linked.open...n/vavai/riv/zamer	http://linked.opendata.cz/resource/domain/vavai/zamer/MSM%20212300013
number of pages	8 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	3-540-40730-8
http://localhost/t...ganizacniJednotka	21230

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